Learning-based 3D reconstruction methods for non-collaborative surfaces-A metrological evaluation

Non-collaborative (i.e., reflective, transparent, metallic, etc.) surfaces are common in industrial production processes, where 3D reconstruction methods are applied for quantitative quality control inspections. Although the use or combination of photogrammetry and photometric stereo performs well for well-textured or partially textured objects, it usually produces unsatisfactory 3D reconstruction results on non-collaborative surfaces. To improve 3D inspection performances, this paper investigates emerging learning-based surface reconstruction methods, such as Neural Radiance Fields (NeRF), Multi-View Stereo (MVS), Monocular Depth Estimation (MDE), Gaussian Splatting (GS) and image-to-3D generative AI as potential alternatives for industrial inspections. A comprehensive evaluation dataset with several common industrial objects was used to assess methods and gain deeper insights into the applicability of the examined approaches for inspections in industrial scenarios. In the experimental evaluation, geometric comparisons were carried out between the reference data and learning-based reconstructions. The results indicate that no method can outperform all the others across all evaluations.